Vertical Cities: Emergent Patterns of Movement and Space Use in Dense Vertically Integrated Urban Built Environments
Bibliographic record
Abstract
In high-density, land-scarce cities like Singapore, the successful translation of ground-level urban qualities and benefits into vertical living is crucial for social, economic, environmental, and ecological sustainability. This research introduces a Network science-based spatial analysis framework to evaluate the connectivity and relationships of vertically integrated urban open spaces. Kampung Admiralty (KA), a unique development integrating housing for the elderly with various facilities, serves as a case study. The methodology combines static spatial network measures and real-world movement data to predict movement flows, accessibility, and connectivity. Lift lobbies and elevated garden connectors emerged as critical paths, effectively distributing pedestrian flows. Landscape spaces played a key role in visual and physical connectivity, offering high recreational and social value. Strategic placement of “social attractors” improved space utilization. The study highlights the importance of spatial design parameters in user-space interactions and provides insights into socio-spatial networks at both ground and elevated levels. It identifies key connectors that facilitate effective planning and design of vertically integrated public space networks, promoting social and spatial effectiveness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".